AI turns cold chain data into predictive power for pharma logistics

The pharmaceutical air cargo industry is looking to move beyond conventional temperature monitoring towards predicting disruptions before they affect product integrity. As medicines become more sensitive to temperature variations and supply chains involve multiple transport and handling partners, logistics companies and pharmaceutical manufacturers are increasingly using data, artificial intelligence (AI) and predictive analytics to identify risks earlier and improve shipment reliability.

The growing importance of temperature-controlled transportation is reflected in figures from the International Air Transport Association (IATA). According to its CDD Enhanced CargoIS data, the share of pharmaceutical air cargo classified as temperature-controlled increased from 59% in 2019 to 82% in 2024, reaching 90% in January-July 2025.

Traditionally, temperature monitoring has relied on data loggers that record conditions during transit and establish whether a shipment has remained within its prescribed temperature range. However, this information may only reveal a problem after it has occurred, leaving limited time for logistics teams to intervene.

Predictive cold chain management aims to address this limitation. By combining historical shipment information with live temperature readings, weather conditions, airport dwell times, flight schedules and carrier performance, companies can assess where a shipment may face risks and take preventive measures.

However, the industry is still at different stages of this transition. While some companies are introducing AI into their monitoring operations, others are focusing on improving data reliability, risk assessments, packaging performance and coordination between supply chain partners.

From monitoring to early intervention
A report published by Pharma.Aero in April 2026, titled IoT Devices and Platforms in Healthcare Logistics, highlights this transition. It says IoT technologies are increasingly being used across healthcare logistics, but their potential remains underused. In many cases, data is still used to monitor what has happened rather than predict and prevent what may happen next.

Dorothee Becher-Schmacke, VP Global Air Logistics Healthcare at Kuehne+Nagel, said one of the biggest risks for healthcare customers was finding out about a shipment problem too late.

“A temperature excursion or disruption that goes unnoticed can compromise product integrity and, in some cases, result in the loss of highly valuable or time-critical medicines,” she said.

Kuehne+Nagel uses active monitoring systems and Internet of Things (IoT) sensor technology to provide real-time visibility across the shipment journey, including temperature conditions and other aspects of product integrity. Its dedicated 24/7 Healthcare HyperCare team monitors potential disruptions and coordinates with airlines, ground handlers and other partners to implement mitigation measures.

The company is also introducing AI into its Healthcare HyperCare operation. Becher-Schmacke said AI agents were being used to contact carriers, obtain temperature and battery readings, confirm storage conditions and check whether shipments had departed on their planned flights. They can also identify delays, shipment splits and rebookings.

This automates repetitive information-gathering tasks, while complex decisions and interventions remain with healthcare specialists.

Kuehne+Nagel is also using risk-management capabilities to assess route resilience. For temperature-sensitive pharmaceuticals, selecting a route involves more than identifying the fastest connection. Logistics teams need to consider transit time, historical performance, temperature deviation risks and carrier reliability.

If a connection is at risk or a delay develops, healthcare teams can coordinate with airlines and ground handlers to arrange alternative temperature-controlled storage, dry-ice replenishment or alternative routing.

Becher-Schmacke said predictive analytics could help identify recurring risks and support future decisions on routes, carriers and packaging. However, technology does not replace specialists in managing temperature-sensitive shipments.

“Predictive analytics supports rather than replaces human decision-making,” she said, adding that technology provides earlier information while experienced healthcare teams assess the situation and decide on the appropriate intervention.

Understanding the risk before a shipment moves
For pharmaceutical manufacturers, temperature risks begin well before a shipment reaches the airport. Products pass through manufacturing facilities, local warehouses, shipping docks, international transportation, destination warehouses and final distribution points. Each stage introduces a potential risk.

The World Health Organization's Technical Report Series 961, Annex 9, sets out guidance for the safe storage and distribution of time- and temperature-sensitive pharmaceutical products, reinforcing the importance of maintaining appropriate conditions throughout the distribution chain.

Vipul Jain, Associate Vice President – Supply Chain Strategy and Analytics at Intas Pharmaceuticals, said temperature management needed to cover the entire distribution chain rather than focus on a single part of the journey.

“It is not just one part and ensuring that every part of the distribution channel is covered,” Jain said.

The risks vary depending on the mode of transportation, service provider capabilities, available infrastructure and the skills of the people handling the shipment. An excursion could occur during local transportation to an airport, while cargo is waiting for an international movement or during delivery from a destination warehouse to the final customer.

Jain said technology was helping companies track environmental conditions and product quality throughout the journey. Data loggers remain important, but the way information from these devices is monitored is equally significant. Systems need to provide reliable information and timely alerts so that teams can respond when a deviation is observed.

Shipment planning also needs to account for possible delays and disruptions. Packaging, transportation partners and their storage infrastructure must be assessed to ensure products are not exposed to unsuitable conditions if transit times change.

Jain also pointed to the value of historical data in improving shipment planning. Past performance can help identify suitable routes and vulnerable points in the journey, while information on geopolitical developments, route conditions and weather can support better decisions. Comparing planned movements with actual performance can further help refine future shipment planning.

Product characteristics shape cold chain decisions
Predictive cold chain management cannot follow a single approach for all pharmaceutical products. Different medicines have different temperature requirements and tolerances, making it important to understand the product before selecting its packaging, route and monitoring arrangements.

Khyati Desai, Senior Manager, Global Trade Compliance at Thermo Fisher Scientific, speaking in her individual capacity, said predictive analytics could help companies assess the likelihood of an excursion based on the route, packaging and seasonal conditions.

Models trained on historical shipment data, weather, airport dwell times and carrier performance can assess shipment risk before departure. This information can help companies adjust packaging configurations, select more reliable routes or flights, and arrange standby handling at transit points.

Desai identified ambient temperature at origin, transit and destination, ground dwell time, packaging type and its tested thermal duration, lane and handler history, flight schedule reliability and customs clearance time as important inputs for assessing temperature risk.

Ground dwell time is particularly relevant during tarmac handling and transfers. A shipment may remain within its permitted temperature range when it leaves the origin facility but faces increasing exposure if it spends longer than planned at an airport or during a customs hold.

Real-time monitoring can help teams act on temperature trends before a shipment crosses its permitted limits. Desai said teams could arrange re-icing, move shipments to cold storage or escalate customs clearance while the product remained within specification. She also highlighted the importance of reducing customs dwell time in protecting product integrity.

The requirements are becoming more complex as pharmaceutical supply chains handle a wider range of temperature-sensitive products. Alongside conventional refrigerated shipments requiring 2–8°C and controlled room temperature conditions, there is growing volume at frozen, ultra-low and cryogenic temperatures for cell and gene therapies and some vaccines.

Desai said these requirements were leading to more specialised packaging systems matched to shipment duration and season, as well as more continuous monitoring. For certain advanced therapies, handling windows are tighter and shipments may be patient-specific and time-critical. Chain-of-custody and chain-of-condition records are also important for batch release decisions.

Preparing for disruptions across air freight routes
The ability to predict risks also depends on how well companies understand the conditions along a particular shipment lane.

Hanoz Tarapore, Country Supply Chain Head – ASEAN, Korea, ANZ, Ortho Clinical Diagnostics, said anticipating a temperature excursion required an understanding of a product's tolerances and its limits for temperature, shock, tilt and humidity.

For a new shipment lane, the company conducts a risk assessment covering carriers, aircraft types, airport facilities, cargo handling and ground transportation. Each point is assessed to identify where temperature excursions, delays or physical handling incidents could occur. Business continuity plan (BCP) measures are then put in place to address these risks.

Packaging is also selected to provide sufficient additional cold chain validity to cover possible delays. Tarapore said companies needed to understand the characteristics of each product, including the type and range of excursion it could withstand, before deciding on the most suitable and cost-efficient response.

For particularly sensitive cargo or high-risk shipment lanes, real-time monitoring can help teams take corrective action when an event occurs. The response depends on the product's tolerance, the duration of exposure and the conditions under which it has been stored.

This makes scenario planning important before a shipment moves. Companies need to identify the probability of disruption at different stages and establish what action can be taken if it occurs.

Fragmented data remains a major obstacle
Even when monitoring systems are available, information may not move seamlessly between the different companies involved in a shipment. Airlines, airports, ground handlers, freight forwarders and pharmaceutical manufacturers often operate separate systems, making it difficult to maintain a consistent view of cargo movements.

The Pharma.Aero report on IoT devices and platforms identifies fragmented systems, limited interoperability, inconsistent connectivity and gaps in data standardisation among the barriers to wider digital adoption in healthcare logistics. It also highlights cybersecurity, compliance and digital skills as areas requiring attention.

Kuehne+Nagel said end-to-end visibility depended on timely data exchange between all parties. Its HealthChain network provides a quality framework with standardised GxP-compliant processes across more than 270 certified locations in over 60 countries. The company also highlighted cooperation with airlines and ground handling partners as important to maintaining standards throughout the journey.

Intas said the use of multiple service providers had historically created gaps in traceability because each partner could have different infrastructure and digital capabilities. Jain said interconnected technology networks were improving the ability to track shipments across different partners.

At Ortho Clinical Diagnostics, Tarapore identified a specific gap between airlines and logistics service providers. While logistics providers receive milestone notifications during the journey, airline systems may not always be updated promptly when a shipment is delayed, offloaded or only partially loaded.

This can leave tracking systems showing that a shipment is on schedule even when the cargo is not expected to arrive at the planned time. Such delays in operational updates can limit the effectiveness of predictive systems because teams may not receive accurate information in time to arrange an intervention.

Desai said manufacturers, forwarders and airlines needed common data standards and shared milestone definitions, including handover times and acceptance temperatures. Lane-level performance and exception data also needed to be shared under clear data-governance arrangements.

APIs or neutral platforms could help monitoring information reach all the parties responsible for taking action. Joint procedures should also define who intervenes, at what threshold and within what time.

“Prediction only works when the data is timely and the right party is empowered to act,” Desai said.

The barriers extend beyond technology. Desai identified fragmented data, reluctance to share operational information, legacy systems, the cost of sensors and validated packaging, and a shortage of professionals with expertise in both pharmaceutical quality requirements and data science.

A Pharma.Aero industry paper published in September 2026, AI in Pharmaceutical & MedTech Supply Chains – From Capability to Accountability, also examines the challenges of moving AI initiatives from pilot projects to wider operational use. It identifies data and systems, organisational readiness, governance, compliance and coordination between supply chain partners as factors that can limit adoption.

The paper argues that AI adoption needs to develop across technology and data, human capability, and governance and compliance. In pharmaceutical logistics, where decisions can affect product integrity and regulatory compliance, predictive systems need reliable information and clear accountability for interventions.

Measuring the impact of predictive technology
As more companies invest in digital cold chain solutions, measuring their actual impact will become important. The availability of real-time data or automated alerts does not, by itself, demonstrate that a system is reducing temperature excursions or preventing product losses.

Kuehne+Nagel said digital solutions were providing earlier visibility of risks and helping its specialists respond more consistently. IoT sensors also provide information on shipment location and conditions such as temperature, humidity and shock. The company said this allows specialists to spend less time gathering information and more time managing exceptions. However, it did not provide specific figures on reductions in temperature excursions or product losses.

Desai said the industry should assess predictive technology through measures such as temperature excursions per thousand shipments, product loss and write-off values, the share of at-risk shipments identified and successfully managed, false alert rates, claims and investigation times, and on-time-in-full performance.

She said results should be compared with a baseline and, where possible, reviewed independently to distinguish measurable improvements from technology providers' claims.

For Intas, historical data and comparisons between planned and actual shipment performance can help refine future route and partner decisions. Jain said technology could help companies identify routes that were safer and faster for particular products while improving planning based on previous deviations.

The transition from monitoring to prediction will depend on more than the adoption of AI. It will require reliable data, suitable packaging and infrastructure, common standards and stronger coordination between airlines, handlers, forwarders and pharmaceutical companies.

For temperature-sensitive medicines, the value of predictive cold chain management will ultimately be measured by whether it can help prevent disruptions from becoming product integrity problems.